1. What is a Software Engineer at Dataiku?

At Dataiku, the Software Engineer role is central to the company’s mission of "Everyday AI." You are not just building a backend service or a frontend widget; you are engineering the platform that allows organizations to systemize the use of data and AI. Dataiku’s flagship product, the Data Science Studio (DSS), is a complex, integrated development environment for data professionals. As an engineer here, you work on a platform that abstracts complex big data and machine learning technologies (like Spark, Kubernetes, and various cloud providers) into an accessible, collaborative interface.

This position requires a unique blend of deep technical expertise and product versatility. Whether you are working on the core computation engine, the visual interface, or the connectivity layer that integrates with external data sources, your work directly impacts how data scientists, analysts, and engineers collaborate. The engineering culture values craftsmanship and robustness, as the software is deployed in diverse and often restrictive enterprise environments. You will face challenges related to scalability, concurrency, and the orchestration of complex data pipelines.

2. Getting Ready for Your Interviews

Preparation for Dataiku is distinct because the company places a massive emphasis on practical engineering skills over pure theoretical knowledge. You should approach this process not just as a test of your coding speed, but as an audit of your ability to deliver production-grade software.

To succeed, focus on demonstrating the following key evaluation criteria:

Production-Grade Engineering Dataiku evaluates whether you can write code that is ready for the real world. This means your solutions must be clean, maintainable, and robust. Interviewers look for proper error handling, logging, meaningful variable naming, and—crucially—comprehensive unit testing. A working solution that is messy or lacks tests is often grounds for rejection.

Algorithmic Proficiency (Graph Theory) While the focus is practical, strong algorithmic foundations are required. Specifically, you should be comfortable with graph algorithms (e.g., shortest path, traversal, cycle detection) and data structures. You will likely need to apply these algorithms to solve a business-logic problem rather than a generic LeetCode puzzle.

Product Centricity and Humility Dataiku values engineers who understand the "why" behind the code. You will be evaluated on your ability to understand user constraints and business logic. Culturally, the team looks for humility and collaboration; candidates who appear arrogant or dismissive of existing solutions—or who fail to constructively discuss trade-offs—often struggle in the behavioral rounds.

3. Interview Process Overview

The interview process at Dataiku is thorough and can be lengthy, often taking several weeks from initial contact to offer. It is designed to filter for candidates who are not only technically capable but also patient and detail-oriented. The process generally begins with a recruiter screen to align on timelines and interest, followed by a screening call with an Engineering Manager or Team Lead. This manager screen is a mix of background review and high-level technical discussion.

The centerpiece of the process is the Take-Home Assessment. Unlike companies that use 45-minute timed coding challenges, Dataiku frequently assigns a substantial project that candidates complete on their own time. This is followed by a "Debrief" or Technical Review interview, where you defend your design decisions, explain your code, and potentially extend the functionality live. If you pass this stage, you will move to final rounds which include cultural fit interviews and conversations with senior leadership (VPs or Directors).

The timeline above illustrates a funnel that relies heavily on the "Technical Assessment" phase. Candidates should budget significant time and energy for the take-home portion, as it is the primary filter. The final rounds are less about coding and more about validating seniority, architectural thinking, and cultural alignment.

4. Deep Dive into Evaluation Areas

The following sections detail the specific areas where you will be tested. These insights are drawn from recent candidate experiences.

The Take-Home Assessment

This is the most critical component of the Dataiku interview loop. You will likely be asked to build a small application or API, or specifically install and configure the Dataiku software to solve a problem.

Be ready to go over:

• Production Standards: You must treat this code as if it were going to production. This includes a README file, setup instructions, dependency management, and a clean project structure.
• Graph Algorithms: A common variation of the assignment involves finding the "best path" or "shortest path" in a weighted graph, sometimes with changing weights or obstacles. You may need to implement Dijkstra’s algorithm or A*.
• API Design: You may be asked to wrap your logic in a REST API. Ensure you use standard HTTP verbs and status codes correctly.
• Testing: Do not skip tests. You are expected to write unit tests for your logic.

Example questions or scenarios:

• "Implement an API that computes the shortest path between two points while avoiding dynamic obstacles."
• "Build a production-ready application that finds the optimal route in a weighted graph where you can 'wait' at nodes for better conditions."
• "Install the Dataiku product in a cloud environment and complete a set of configuration tasks."

Technical Debrief & Code Review

After submitting your assignment, you will have a live session with engineers. They will have reviewed your code in detail.

Be ready to go over:

• Justification: Why did you choose this specific library? Why did you structure your classes this way?
• Debugging: If there were bugs in your submission (even minor UI bugs or console warnings), they will ask you to investigate them live.
• Extension: The interviewers may ask, "How would you handle it if the graph had cycles?" or "How would you scale this if we had a million requests per second?"

Example questions or scenarios:

• "Walk us through your error handling strategy. Why did you log this error but throw an exception for that one?"
• "We found a warning in the console from an external library. How would you resolve it?"

System Design & Architecture

For senior roles, or during the VP interviews, the conversation will shift to high-level design.

Be ready to go over:

• Scalability: How to handle large datasets or high concurrency.
• Integration: How your code interacts with databases, external APIs, or cloud services (AWS/Azure).
• Trade-offs: Discussing consistency vs. availability, or monolith vs. microservices in the context of the Dataiku platform.

5. Key Responsibilities

As a Software Engineer at Dataiku, your daily work involves solving complex technical challenges to empower data teams. You are responsible for designing and implementing features that span the full stack, though many roles lean heavily toward the backend (Java) or the core engine.

You will collaborate closely with Product Managers to translate user needs into technical specifications. A significant part of the role involves ensuring the platform integrates seamlessly with the modern data stack—this could mean writing code that interfaces with Kubernetes clusters, manages Spark jobs, or connects to Snowflake and BigQuery. You are also responsible for maintaining the high quality of the codebase, which includes code reviews, writing automated tests, and ensuring that the software remains stable across the various on-premise and cloud environments where customers deploy it.

6. Role Requirements & Qualifications

Dataiku looks for engineers who are versatile and grounded in computer science fundamentals.

Must-have skills

• Strong Coding Proficiency: Deep expertise in Java (preferred for backend) or Python. JavaScript/TypeScript is essential for full-stack roles.
• Algorithmic Knowledge: Solid understanding of data structures and algorithms, particularly graph theory (BFS, DFS, Dijkstra).
• Engineering Best Practices: Experience with unit testing (JUnit, PyTest), version control (Git), and CI/CD pipelines.
• API Development: Experience designing and consuming RESTful APIs.

Nice-to-have skills

• Cloud Experience: Familiarity with AWS, Azure, or GCP ecosystems.
• Big Data Technologies: Exposure to Spark, Hadoop, Kubernetes, or Docker.
• Frontend Frameworks: Experience with AngularJS (legacy) or React is often helpful for full-stack positions.
• Domain Knowledge: Understanding of the data science lifecycle (ETL, modeling, deployment) is a strong plus.

7. Common Interview Questions

The following questions reflect the patterns observed in Dataiku’s specific interview process. They are not guaranteed to be asked but represent the themes you will encounter.

Technical & Algorithmic

• "Given a graph with weighted edges where weights change over time, find the optimal path between two nodes."
• "How would you detect a cycle in a directed graph?"
• "Explain the time complexity of your shortest-path implementation."
• "How would you modify your algorithm if you were allowed to wait at a node for a specific duration?"
• "Write a function to parse a complex JSON dataset and transform it into a specific object structure."

The "Take-Home" Debrief

• "Why did you choose to use [Library X] instead of the standard library?"
• "If we needed to deploy this application to a cluster, what changes would you make to the configuration?"
• "We noticed you didn't include logging for this specific edge case. What was your reasoning?"
• "How would you improve the performance of this API endpoint under heavy load?"

Behavioral & Cultural

• "Tell me about a time you disagreed with a product manager or technical lead. How did you resolve it?"
• "Why are you interested in Dataiku specifically, compared to other tech companies?"
• "Describe a complex technical concept to someone without a technical background."
• "How do you handle receiving critical feedback on your code?"

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

8. Frequently Asked Questions

Q: How long does the take-home assessment really take? While instructions may say you can take "as much time as you need" or suggest a shorter window (e.g., 4 hours), successful candidates often report spending 8 to 15 hours to ensure the submission is polished, tested, and truly production-ready.

Q: What is the primary reason candidates are rejected after the take-home? The most common reasons are lack of polish and "happy path" programming. Submitting code that works but lacks unit tests, has console warnings, handles errors poorly, or has a messy structure will likely lead to rejection.

Q: Is the interview process remote? Yes, the vast majority of the process is conducted remotely via video calls. However, for the take-home, ensure your local development environment is set up correctly, as you will need to submit a working repository.

Q: Does Dataiku require knowledge of Data Science? It is not strictly required for Software Engineering roles, but it is highly preferred. Showing an understanding of what a data scientist does and the tools they use (Jupyter, Python, SQL) will set you apart.

Q: What is the culture like regarding the interview feedback? Feedback can be mixed. Some candidates receive detailed insights, while others receive generic rejections. During the interview, however, interviewers are generally described as smart and rigorous but friendly.

9. Other General Tips

Treat the Take-Home Like a Job Audit Do not treat the take-home assignment as a casual coding puzzle. Treat it as a pull request that you are submitting to a senior architect at a major tech company. Include a README.md that explains how to run the code, how to run the tests, and your design choices.

Check Your Console and Logs Before submitting your code, run it and watch the console. If there are warnings (even from external libraries) or unhandled stack traces, fix them or suppress them with a documented reason. "Clean code" means a clean runtime environment.

Demonstrate Humility Dataiku places a high value on collaboration. In your interviews, avoid speaking in absolutes. Instead of saying "This is the best way," say "I chose this approach because X, but Y would also be valid if we prioritized Z."

Know the Product Download the free version of Dataiku (DSS) or watch a comprehensive demo before your interview. Understanding the "Flow," "Recipes," and "Datasets" terminology will impress hiring managers and show you've done your homework.

10. Summary & Next Steps

Becoming a Software Engineer at Dataiku is a rewarding challenge that places you at the intersection of software engineering and data science. The role offers the chance to build tools that thousands of companies use to deploy AI. To succeed, you must demonstrate not just coding ability, but engineering maturity—the ability to build robust, tested, and maintainable systems.

Your preparation should prioritize the Take-Home Assessment. Allocate ample time to it, double-check your requirements, and ensure your code is bulletproof. Brush up on your graph algorithms and be prepared to discuss your design choices with humility and clarity. If you approach the process with a focus on quality and user empathy, you will be well-positioned to land the offer.

The compensation data above provides a baseline for the role. Note that Dataiku typically offers a package that includes base salary, a bonus structure, and equity. Compensation can vary significantly based on location and seniority, so use these figures as a reference point for your negotiations. Good luck!